BMJ open · 2026
Development and internal validation of an intrapartum caesarean risk prediction model to guide rural obstetric transfer decisions: a population-based study using BORN Ontario data.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Jolin-Dahel Kheira, Hawken Steven, Bruce Liam
Аннотация
OBJECTIVE: To develop and internally validate a clinical prediction model estimating the probability of emergency caesarean delivery among low-risk pregnancies using routinely available intrapartum variables. The model is intended to support clinicians in recommending transfer decisions in rural obstetric settings without onsite caesarean capacity.
DESIGN: Retrospective cohort study with multivariable logistic regression and internal validation using bootstrap resampling.
SETTING: Province-wide birth registry in Ontario, Canada.
PARTICIPANTS: Singleton, hospital births from low-risk pregnancies between 1 April 2012 and 31 March 2020, captured in the Better Outcomes Registry and Network Ontario. Exclusions included previous caesarean, major comorbidities and other high-risk conditions.
PRIMARY OUTCOME: Emergency caesarean delivery during labour.
RESULTS: Among 611 644 low-risk pregnancies, 66 482 (10.9%) resulted in emergency caesarean delivery. Key predictors included abnormal fetal health surveillance, nulliparity, gestational hypertension, polyhydramnios area under the receiver operating characteristic curve (AUC) and oxytocin augmentation. The optimism-corrected AUC was 0.86, indicating strong discrimination. Calibration was generally good, though the model underestimated caesarean risk in patients first admitted to level I hospitals.
CONCLUSIONS: This study presents a proof-of-concept prototype with strong discrimination, though calibration of the full model was suboptimal in level I hospitals, the intended setting for application. While not clinically deployable in its current form, the model lays the groundwork for a translational pathway that will require local recalibration, external validation and contextual adaptation before implementation. Threshold-based clinical utility (eg, positive/negative predictive value or net benefit) was not assessed and will be evaluated during external validation and local recalibration.
DESIGN: Retrospective cohort study with multivariable logistic regression and internal validation using bootstrap resampling.
SETTING: Province-wide birth registry in Ontario, Canada.
PARTICIPANTS: Singleton, hospital births from low-risk pregnancies between 1 April 2012 and 31 March 2020, captured in the Better Outcomes Registry and Network Ontario. Exclusions included previous caesarean, major comorbidities and other high-risk conditions.
PRIMARY OUTCOME: Emergency caesarean delivery during labour.
RESULTS: Among 611 644 low-risk pregnancies, 66 482 (10.9%) resulted in emergency caesarean delivery. Key predictors included abnormal fetal health surveillance, nulliparity, gestational hypertension, polyhydramnios area under the receiver operating characteristic curve (AUC) and oxytocin augmentation. The optimism-corrected AUC was 0.86, indicating strong discrimination. Calibration was generally good, though the model underestimated caesarean risk in patients first admitted to level I hospitals.
CONCLUSIONS: This study presents a proof-of-concept prototype with strong discrimination, though calibration of the full model was suboptimal in level I hospitals, the intended setting for application. While not clinically deployable in its current form, the model lays the groundwork for a translational pathway that will require local recalibration, external validation and contextual adaptation before implementation. Threshold-based clinical utility (eg, positive/negative predictive value or net benefit) was not assessed and will be evaluated during external validation and local recalibration.